A DNN-based 5G MIMO system adopting a mix of tactics

The emergence of 5G technology necessitates advanced communication systems that can handle high data rates while providing improved reliability. However, current methods often struggle with issues related to complexity and efficiency. This paper addresses these challenges by developing a multiple input multiple output (MIMO) system that integrates deep neural network (DNN) demappers, low-density parity-check (LDPC) coding, and polar coding, creating a mix of tactics aimed at boosting performance in flat fading channel environments. The main objectives of this study are to improve the bit error rate (BER) and spectral efficiency across different orders of quadrature amplitude modulation (QAM)—specifically, 4-QAM, 16-QAM, and 256-QAM. The proposed system employs a linear minimum mean square error (LMMSE) equalizer to ensure effective signal detection. Simulation results indicate that the LDPC coding technique significantly outperforms polar coding across all evaluated QAM modulation orders, highlighting its effectiveness in enhancing system performance. An analysis of BER and spectral efficiency demonstrates considerable reliability and data throughput improvements, making the MIMO system particularly well-suited for the demanding requirements of 5G applications. The findings suggest that integrating advanced coding strategies with neural demapping can effectively tackle the complexities introduced by the 5G environment. This research establishes a foundation for future studies and the development of robust communication systems capable of supporting the next generation of wireless technology, thereby enabling a wide range of applications, from enhanced mobile broadband to extensive IoT connectivity.